Bi-Order Multimodal Integration of Single-Cell Data

Authors

Jinzhuang Dou
Shaoheng Liang
Vakul Mohanty
Qi Miao
Yuefan Huang
Qingnan Liang
Xuesen Cheng
Sangbae Kim
Jongsu Choi
Yumei Li
Li Li
May Daher
Rafet Basar
Katayoun Rezvani
Rui Chen
Ken Chen

Publication Date

5-9-2022

Journal

Genome Biology

DOI

10.1186/s13059-022-02679-x

PMID

35534898

PMCID

PMC9082907

PubMedCentral® Posted Date

5-9-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Keywords

Single-cell multi-omics, Bi-order canonical correlation analysis, Cell type identity

Published Open-Access

yes

Recommended Citation

Dou, Jinzhuang; Liang, Shaoheng; Mohanty, Vakul; et al., "Bi-Order Multimodal Integration of Single-Cell Data" (2022). Faculty, Staff and Student Publications. 2791.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/2791